1 / 10

Incrementally Learning Parameter of Stochastic CFG using Summary Stats

Incrementally Learning Parameter of Stochastic CFG using Summary Stats. Written by:Brent Heeringa Tim Oates. Goals:. To learn the syntax of utterances Approach : SCFG (Stochastic Context Free Grammar) M=<V,E,R,S> V-finite set of non-terminal E-finite set of terminals

kane-hebert
Download Presentation

Incrementally Learning Parameter of Stochastic CFG using Summary Stats

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Incrementally Learning Parameter of Stochastic CFG using Summary Stats Written by:Brent Heeringa Tim Oates

  2. Goals: • To learn the syntax of utterances Approach: • SCFG (Stochastic Context Free Grammar) M=<V,E,R,S> V-finite set of non-terminal E-finite set of terminals R-finite set of rules, each r has p(r). Sum of p(r) of the same left-hand side = 1 S-start symbol

  3. Problems with most SCFG Learning Algorithms 1)Expensive storage: need to store a corpus of complete sentences 2)Time-consuming: algorithms needs to repeat passes throughout all data

  4. Learning SCFG • Inducing context-free structure from corpus(sentences) • Learning – the production(rules) probabilities

  5. General method: Inside/Outside algorithm Expectation-Maximization (EM) Find expectation of rules Maximize the likelihood given both expectation & corpus Disadvantage of Inside/Outside algo. Entire sentence corpus must be stored using some representation(eg. chart parse) Expensive storage (unrealistic for human agent!) Learning SCFG –Cont

  6. Proposed Algorithm • Use Unique Normal Form (UNF) • Replace all terminal A-z to 2 new rules • A->D p[A->D]=p[A->z] • D-> z p[D->z]=1 • No two productions have the same right hand side

  7. Learning SCFG- Proposed Algorithm -cont • Use Histogram • Each rule has 2 histograms (Hor, HLr)

  8. Proposed Algorithm -cont • Hor -contructed when parsing sentences in O • HLr- -will continue to be updated throughout learning process • HLr rescale to fixed size h • Why?! • Recently used rules has more impact on histogram

  9. Comparing between HLr & Hor • Relative entropy • T decrease- increase prob of rules used • (if s large, increase prob of rules used when parsing last sentence ) • T increase- decrease prob of rules used (eg pt+1(r)=0.01* p t+1(r)

  10. Inside/Outside O(n3) Good 3-5 iterations Bad Need to store complete sentence corpus Proposed Algo O(n3) Bad 500-1000 iterations Good Memory requirements is constant! Comparing Inside/Outside Algo with the proposed algorithm

More Related